Pragmatic Decision Support Tools for Cyber Consequence and Risk Analysis.
Abstract not provided.
Reinhardt, Jason Christian↗
SEARCH · Engineering Papers
Results for “Analysis Tool”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Radiation data analysis with InterSpec – and some related tools
Explore the source record for details and available documents.
Johnson, William Casey↗
Airport Ground Support Equipment Infrastructure & Logistics Electrification Assessment Tool: 2025 Data Development, Modeling and Analysis for DFW
The aviation industry is increasingly turning to modernize freight facilities by integrating electric Ground Support Equipment (eGSE) to enhance operational efficiency of freight facility moving vehicles and equipment. Airports worldwide are adopting eGSE to streamline cargo movement, reduce fuel and maintenance costs, and improve logistics coordination.1 North America, with its advanced aviation infrastructure, leads this transition, leveraging Internet of things (IoT)-enabled automation and zero emission technologies to boost reliability and reduce human errors.2 Electrification of freight facility moving vehicles and equipment boosts turnaround times, improves equipment reliability, and optimizes logistics coordination, giving operators a competitive advantage. With rising fuel price volatility and the pressure to meet stringent performance benchmarks, airports are focusing on cost-effective, scalable solutions for long-term financial and operational gains. To further accelerate electrification, airports are integrating Zero Emission Vehicles (ZEVs) into rental car fleets and deploying electric baggage carts, requiring strategic investments in charging infrastructure. 3 The shift, however, presents challenges, such as limited technical expertise, high capital costs, and complex procurement processes. By forging strategic partnerships, leveraging advanced technologies, and optimizing infrastructure investments, airports can create a resilient, future-ready ecosystem that enhances the movement of people and goods through electrification-driven efficiency. Supported by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO), this electrification effort provides a scalable, cost-effective solution to improve airport freight operations. Through targeted investments and innovation, airports enhance efficiency, reduce costs, and meet performance benchmarks while advancing toward a resilient, electrified future.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Hyper-Differential Sensitivity Analysis of Uncertain Parameters in PDE-Constrained Optimization
Many problems in engineering and sciences require the solution of large scale optimization constrained by partial differential equations (PDEs). Though PDE-constrained optimization is itself challenging, most applications pose additional complexity, namely, uncertain parameters in the PDEs. Uncertainty quantification (UQ) is necessary to characterize, prioritize, and study the influence of these uncertain parameters. Sensitivity analysis, a classical tool in UQ, is frequently used to study the sensitivity of a model to uncertain parameters. In this article, we introduce "hyper-differential sensitivity analysis" which considers the sensitivity of the solution of a PDE-constrained optimization problem to uncertain parameters. Our approach is a goal-oriented analysis which may be viewed as a tool to complement other UQ methods in the service of decision making and robust design. We formally define hyper-differential sensitivity indices and highlight their relationship to the existing optimization and sensitivity analysis literatures. Assuming the presence of low rank structure in the parameter space, computational efficiency is achieved by leveraging a generalized singular value decomposition in conjunction with a randomized solver which converts the computational bottleneck of the algorithm into an embarrassingly parallel loop. Two multi-physics examples, consisting of nonlinear steady state control and transient linear inversion, demonstrate efficient identification of the uncertain parameters which have the greatest influence on the optimal solution.
97 MATHEMATICS AND COMPUTING↗
CORAL: A framework for rigorous self-validated data modeling and integrative, reproducible data analysis
Abstract Background Many organizations face challenges in managing and analyzing data, especially when relevant datasets arise from multiple sources and methods. Analyzing heterogeneous datasets and additional derived data requires rigorous tracking of their interrelationships and provenance. This task has long been a Grand Challenge of data science and has more recently been formalized in the FAIR principles: that all data objects be Findable, Accessible, Interoperable, and Reusable, both for machines and for people. Adherence to these principles is necessary for proper stewardship of information, for testing regulatory compliance, for measuring the efficiency of processes, and for facilitating reuse of data-analytical frameworks. Findings We present the Contextual Ontology-based Repository Analysis Library (CORAL), a platform that greatly facilitates adherence to all 4 of the FAIR principles, including the especially difficult challenge of making heterogeneous datasets Interoperable and Reusable across all parts of a large, long-lasting organization. To achieve this, CORAL's data model requires that data generators extensively document the context for all data, and our tools maintain that context throughout the entire analysis pipeline. CORAL also features a web interface for data generators to upload and explore data, as well as a Jupyter notebook interface for data analysts, both backed by a common API. Conclusions CORAL enables organizations to build FAIR data types on the fly as they are needed, avoiding the expense of bespoke data modeling. CORAL provides a uniquely powerful platform to enable integrative cross-dataset analyses, generating deeper insights than are possible using traditional analysis tools.
97 MATHEMATICS AND COMPUTING↗
Raman Spectroscopy Analysis of the Structure and Surface Chemistry of Ti3C2Tx MXene
Raman spectroscopy is one of the most useful tools for the analysis of two-dimensional (2D) materials. While MXenes are a very large family of 2D transition metal carbides and nitrides, there have been just a few Raman studies of materials from this family. Here, we report on a systematic study of the most widely used and most important MXene to date: Ti 3 C 2 T x . By synthesizing material using different methods, we show that Raman spectra of Ti 3 C 2 T x are affected not only by the composition and surface groups but also by intercalated species and stacking. Due to a plasmonic peak of Ti 3 C 2 T x around 785 nm, resonant conditions are achieved, enabling us to observe an extra peak at ~120 cm –1 , when excited with a red diode laser. We report differences in Raman spectra collected from single flakes of Ti 3 C 2 T x , colloidal solutions, and multilayer films. Lastly, we show how an undesirable photoluminescent background could serve as evidence of material degradation, which leads to the formation of defective titania and amorphous carbon. Overall, this study shows how Raman spectroscopy can be used for the characterization of important emerging 2D materials: MXenes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Fixed-Energy Response-Function Analysis with Multi-Efficiency (FRAM) Software, Version 6
Researchers at Los Alamos National Laboratory have developed FixedEnergy Response- Function Analysis with Multiple Efficiency (FRAM), a powerful software tool for gamma-ray spectrometry isotopic analysis. This tool leverages Los Alamos expertise in nuclear safeguards, radiation detection systems, and related analysis techniques. A household name in the non-destructive assay community for over two decades, FRAM determines a special nuclear material source's isotopic composition by analyzing gamma-ray spectra. Los Alamos researchers have released the latest version of FRAM that incorporates several updates to the 2013 major release of FRAM, Version 5.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method
Electrochemical impedance spectroscopy (EIS) is a key tool across various scientific disciplines, including energy sciences, chemistry, and biology, enabling the analysis of electrochemical systems. However, conventional methods for interpreting EIS data are often complex and model dependent. The distribution of relaxation times (DRT) offers a non-parametric approach that simplifies the interpretation process by providing a timescale interpretation of EIS data. This article provides a comprehensive review of current methods for DRT inversion. Additionally, a survey of practitioners highlights key challenges in the field. Here, the findings underscore the need for standardized DRT analysis and benchmarks, as well as the development of automated analysis tools. These advancements would improve the usability and interpretability of EIS data. Ultimately, implementing these improvements could not only propel the field forward but also expand the application of DRT in scientific research by making it accessible to a broader range of researchers, including those without specialized expertise in programming or statistics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine Learning for Distributed Acoustic Sensing data (MLDAS) v1.0.1
MLDAS is a Python-written package for exploratory data analysis and deep learning training on Distributed Acoustic Sensing data. The machine learning tools are powered by the PyTorch library and designed to work efficiently on large scale datasets using parallel computing. Various SLURM scripts as well as a tutorial have also been made available to allow geophysicists to quickly and easily implement the available tools in their analysis workflow on supercomputer facilities.
Dumont, Vincent↗
Impact of Porcine Arterivirus, Influenza B, and Their Coinfection on Antiviral Response in the Porcine Lung
Interferon (IFN) cytokines induce an autonomous antiviral state in cells of the infected site to restrict virus spreading and critically regulate overall antiviral response. The antiviral state leads to host protection through expression of hundreds of IFN-stimulated genes that restrict viral infection through multiple mechanisms, for example, directly in viral genome degradation and indirectly through cellular metabolic inhibition. Young pigs were split into four treatment groups: control, porcine reproductive and respiratory syndrome virus (PRRSV, also known as porcine arterivirus) infected, influenza B virus (IBV) infected, and IBV/PRRSV coinfection. Lung tissue was collected at 3, 5, and 7 days post infection (dpi) for control, PRRSV and IBV/PRRSV coinfection, and at 3 and 5 dpi for IBV. Transcriptomic analysis, using usegalaxy.org tools, was performed against the S.scrofa 11.1 reference genome. Differentially expressed gene (DEG) analysis was carried out using DeSeq2 based on the model treatment + dpi + treatment:dpi + E. Downstream analysis examined the interaction of DEG at each dpi for over-enriched gene ontology (G.O.) terms and pathways. Comparisons of the infected groups vs. the controls yielded a total of (n = 1412) DEGs for the PRRSV group and (n = 1578) for the IBV/PRRSV group across all timepoints. The IBV group had (n = 64) total DEGs across 3 and 5 dpi. Expression data were considered statistically significant based on false discovery rate (FDR) ⫹ 0.1. Venn diagram comparisons of the DEGs across dpi showed that groups shared only 16 DEGs at 3 dpi, no DEGs were shared at 5 dpi, and for 7 dpi, only the PRRSV and IBV/PRRSV groups were compared and shared a total of 43 DEGs. Across the comparisons, differential expression was observed in antiviral genes such as IRF1, MX1, and OAS2. The IBV and IBV/PRRSV groups showed higher expression of antiviral genes at earlier dpi than the PRRSV group. Additionally, downregulated genes from the comparisons clustered around Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways effecting lung development and cellular integrity. Early expression of host IFN and antiviral genes may lead to viral RNA degradation, and assembly and transcription inhibition in the IBV infections. In comparison, expression of antiviral genes in the PRRSV group decreased across time. The decrease may explain why PRRSV infections persist, while IBV clears. Moreover, all infected groups showed prolonged upregulation in neutrophil degranulation pathway activity, possibly exacerbating symptomatic lung lesion pathology seen in these respiratory infections.
59 BASIC BIOLOGICAL SCIENCES↗
Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption (Research Performance Final Report)
This is the research performance final report for the project entitled: Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption This project was able to achieve the DOE’s goals of developing new modeling tools to understand MDHD vehicle operation and adoption. The first modeling tool is a fleet-level techno-economic analysis model capable of estimating energy use and associated environmental and cost impacts for electrified and conventional vehicles of any MDHD vocation, using real-world cost and operations data, including approaches to optimizing schedules for charging and/or vehicle dispatch. The second modeling tool is a system-level, bottom-up, agent-based adoption model capable of generating geographically-resolved estimates of market projections for MDHD vehicles and charging infrastructure. These tools will be developed and published to serve dual purposes as analysis tools for researchers, and decision-support tools for decision makers within the MDHD system.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
FLARE: field line analysis and reconstruction for 3D boundary plasma modeling
The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D boundary plasma codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations (stellarators) and those with symmetry breaking perturbations of an axisymmetric equilibrium (tokamaks) are supported. The code itself is written in Modern Fortran with MPI support for parallel computing, and it incorporates object-oriented programming for the definition of the magnetic field and the material surface geometry. Extended derived types for a number of different magnetohydrodynamic equilibrium and plasma response models are implemented. The core element of FLARE is a field line tracer with adaptive step-size control, and this is integrated into tools for the construction of Poincaré maps and invariant manifolds of X-points. A collection of high-level procedures that generate output files for visualization is build on top of that. The analysis modules are build with Python frontends that facilitate customization of tasks and/or scripting of parameter scans.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Modeling Radionuclide Inventories in MSR Off-Gas Systems with Radiochemical Transport Analysis
Molten salt reactors (MSR) contain unique characteristics that may require enhancements to modeling tools to accurately predict phenomena. One characteristic that may be advantageous to leverage during normal operation is on-line processing of the circulating fuel salt, such as an off-gas system (OGS) to remove volatile fission products. Therefore, new modeling tools must be developed to integrate spatial resolution and chemistry effects into fuel depletion tools to be able to account for these non-core sources of radioactivity. Such types of radiochemical transport analysis tools were used to estimate the removal rates for 12 elements within a flow model of the Molten Salt Reactor Experiment (MSRE) by optimizing against legacy experimental data of the gas-borne (GB) percentages of 12 nuclides. The removal rates were used in a depletion model to calculate the FP inventory that enters the MSRE OGS. Calculations are in good agreement with the empirical GB percentages reported for the 12 nuclides, which validates the approach and verifies each tool’s treatment of the radiochemical flow effects. The OGS inventory is discussed in terms of the largest nuclide contributors to activity, dose consequence, decay heat, and elemental composition. Finally, insights from the study allow recommendations to be made for future code development activities.
Shahbazi, Shayan↗
In-Transit Data Transport Strategies for Coupled AI-Simulation Workflow Patterns
Coupled AI-Simulation workflows are becoming the major workloads for HPC facilities, and their increasing complexity necessitates new tools for performance analysis and prototyping of new in-situ workflows. We present SimAI-Bench, a tool designed to both prototype and evaluate these coupled workflows. In this paper, we use SimAI-Bench to benchmark the data transport performance of two common patterns on the Aurora supercomputer: a one-to-one workflow with co-located simulation and AI training instances, and a many-to-one workflow where a single AI model is trained from an ensemble of simulations. For the one-to-one pattern, our analysis shows that node-local and DragonHPC data staging strategies provide excellent performance compared Redis and Lustre file system. For the many-to-one pattern, we find that data transport becomes a dominant bottleneck as the ensemble size grows. Our evaluation reveals that file system is the optimal solution among the tested strategies for the many-to-one pattern.
Tummalapalli, Harikrishna [Argonne National Labora↗
A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs
Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.
computer vision↗
Overview of System Integration Analysis Activities for Integrated Waste Management
Spent nuclear fuel (SNF) generated by the current fleet of commercial nuclear reactors is being stored at the reactor sites in spent fuel pools (SFPs) and in dry independent spent fuel storage installations (ISFSIs). The US Department of Energy Office of Nuclear Energy (DOE-NE) is developing an Integrated Waste Management Program (IWMP) comprising a suite of options and supporting analyses to enable future informed choices. The IWMP is organized into the following five major areas: 1) Consent-Based Siting, 2) IWM Facilities and Equipment Concepts and Development, 3) Transportation Capability Analysis and Support, 4) Information Technology Solutions and Support, and 5) System Integration Analysis and Support. This paper discusses the activities ongoing in the IWMP System Integration Analysis and Support area. Two main areas of research in system integration are data and tools development, as well as system analysis assessments. One of the tools being developed in the system integration area is the Used Nuclear Fuel-Storage, Transportation & Disposal Analysis Resource and Data System (UNF-ST&DARDS) tool. It is being developed as a foundational resource for DOE-NE to manage SNF data, along with several compatible analysis tools for time-dependent characterization of SNF and related systems. UNF-ST&DARDS has the unparalleled ability to track SNF through the entire back end of the fuel cycle—from the time the fuel is discharged from a reactor through its disposal in a geological repository. UNF ST&DARDS interfaces with the SCALE code system for nuclear analysis and COBRA-SFS for thermal analysis. Another main tool being developed is the Next Generation System Analysis Model (NGSAM). NGSAM is an agent-based simulation software tool expressly designed to be capable of modeling the waste management system. NGSAM has been developed to enable informed decision-making by providing the capability of analyzing various potential system options for the management of SNF and HLW. Using NGSAM, system architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle (i.e., transportation, storage, and disposal). System analysis assessments may investigate the implications of various strategies such as different acceptance rates, acceptance queues, facility capacities and options, standardized canisters, and different assumed system operation start dates. Recently, some system analysis effort has begun to look at how the waste management system might operate for advanced reactor fuel cycles.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
The Fe 4+ / 3+ Redox Mechanism in NaFeO 2 : A Simultaneous Operando Nuclear Resonance and X-ray Scattering Study
Simultaneous operando Nuclear Forward Scattering and transmission X-ray diffraction and 57 Fe Mössbauer spectroscopy measurements were carried out in order to investigate the electrochemical mechanism of NaFeO 2 vs. Na metal using a specifically designed in situ cell. The obtained data were analysed using an alternative and innovative data analysis approach based on chemometric tools such as Principal Component Analysis (PCA) and Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS). This approach, which allows the unbiased extraction of all possible information from the operando data, enabled the stepwise reconstruction of the independent “real” components permitting the description of the desodiation mechanism of NaFeO 2 . This wealth of information allows a clear description of the electrochemical reaction at the redox-active iron centres, and thus an improved comprehension of the cycling mechanisms of this material vs. sodium.
25 ENERGY STORAGE↗
REopt Lite Web Tool: Capabilities and Features
REopt Lite transforms complex energy project decisions into actionable results for building owners, utilities, and industry. Based on decades of NREL decision-support expertise, the free, publicly available REopt Lite web tool guides investment in economic, resilient energy technologies. And open source and API access to the tool enables energy analysis at scale. This presentation provides an overview of how REopt Lite can help users optimize the economic and resilience benefits of distributed energy resources.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗